強化 EA JSON fallback 與 EDM cache 自癒

services/elephant_alpha_orchestrator.py

@@ -15,6 +15,7 @@ Position: Super Orchestrator above Hermes/NemoTron/OpenClaw
 import os
 import json
 import asyncio
+from json import JSONDecodeError
 from datetime import datetime, timedelta
 from typing import Dict, List, Any, Optional
 from dataclasses import dataclass
@@ -241,8 +242,22 @@ BUSINESS OBJECTIVE: Optimize e-commerce performance through intelligent automati
             if not response.success:
                 raise RuntimeError(response.error)
 
-            # Parse and validate response
-            decision_data = json.loads(response.content)
+            # Parse and validate response. Some NIM-compatible models still wrap
+            # JSON in fenced blocks or prepend reasoning text even with json_mode.
+            try:
+                decision_data = self._extract_json_object(response.content)
+            except ValueError as parse_error:
+                logger.warning(
+                    "[ElephantAlpha] Coordination JSON parse failed; using evidence fallback. "
+                    "model=%s error=%s preview=%r",
+                    response.model,
+                    parse_error,
+                    (response.content or "")[:240],
+                )
+                return self._fallback_decision(
+                    business_context,
+                    reason="Elephant Alpha 回應不是可解析 JSON，已改用實證 fallback。",
+                )
             decision = self._parse_strategic_decision(decision_data)
             
             # Log decision for learning
@@ -253,7 +268,46 @@ BUSINESS OBJECTIVE: Optimize e-commerce performance through intelligent automati
         except Exception as e:
             logger.error(f"[ElephantAlpha] Coordination failed: {e}")
             # Fallback to conservative decision
-            return self._fallback_decision(business_context)
+            return self._fallback_decision(
+                business_context,
+                reason=f"Elephant Alpha 協調失敗：{type(e).__name__}",
+            )
+
+    @staticmethod
+    def _extract_json_object(raw: str) -> Dict[str, Any]:
+        """Extract one JSON object from tolerant LLM output."""
+        text_value = (raw or "").strip()
+        if not text_value:
+            raise ValueError("empty response")
+
+        if text_value.startswith("```"):
+            lines = text_value.splitlines()
+            if lines and lines[0].strip().startswith("```"):
+                lines = lines[1:]
+            if lines and lines[-1].strip().startswith("```"):
+                lines = lines[:-1]
+            text_value = "\n".join(lines).strip()
+
+        try:
+            parsed = json.loads(text_value)
+            if isinstance(parsed, dict):
+                return parsed
+            raise ValueError("JSON root is not an object")
+        except JSONDecodeError:
+            pass
+
+        decoder = json.JSONDecoder()
+        for idx, char in enumerate(text_value):
+            if char != "{":
+                continue
+            try:
+                parsed, _end = decoder.raw_decode(text_value[idx:])
+            except JSONDecodeError:
+                continue
+            if isinstance(parsed, dict):
+                return parsed
+
+        raise ValueError("no JSON object found")
     
     def _build_coordination_prompt(self, context: Dict[str, Any]) -> str:
         """Build detailed coordination prompt for Elephant Alpha"""
@@ -431,22 +485,56 @@ Provide your strategic decision in the specified JSON format.
         finally:
             session.close()
     
-    def _fallback_decision(self, context: Dict[str, Any]) -> StrategicDecision:
+    @staticmethod
+    def _context_concrete_actions(context: Dict[str, Any]) -> List[str]:
+        conditions = context.get("conditions") if isinstance(context, dict) else {}
+        if not isinstance(conditions, dict):
+            return []
+        for key in ("_prefetched_hermes_threats", "_db_evidence_actions"):
+            actions = conditions.get(key)
+            if isinstance(actions, list):
+                cleaned = [str(action).strip() for action in actions if str(action).strip()]
+                if cleaned:
+                    return cleaned[:5]
+        return []
+
+    def _fallback_decision(self, context: Dict[str, Any], *, reason: str = "Elephant Alpha unavailable") -> StrategicDecision:
         """Fallback decision if Elephant Alpha fails"""
+        concrete_actions = self._context_concrete_actions(context)
+        trigger_type = str((context or {}).get("trigger_type") or "unknown")
+        if concrete_actions:
+            return StrategicDecision(
+                priority="high",
+                agents_required=["hermes", "elephant_alpha"],
+                reasoning=(
+                    f"{reason} 已保留 {len(concrete_actions)} 筆 DB/Hermes 價格比對實證；"
+                    "僅送人工覆核，不執行自動調價。"
+                ),
+                expected_outcome="產生可稽核的人工覆核告警，避免使用無法解析的 LLM 推論文字。",
+                confidence=0.74,
+                execution_plan=[],
+                resource_requirements={
+                    "compute_cost": "$0.00",
+                    "time_estimate": "人工覆核",
+                    "human_oversight": "required",
+                },
+            )
+
         return StrategicDecision(
             priority="medium",
-            agents_required=["openclaw"],
-            reasoning="Elephant Alpha unavailable, using conservative OpenClaw strategy",
-            expected_outcome="Basic strategic analysis",
+            agents_required=["elephant_alpha"],
+            reasoning=(
+                f"{reason} trigger={trigger_type}，且沒有可稽核的 DB/Hermes 實證；"
+                "不產生策略型行動計畫，避免把推測當成事實。"
+            ),
+            expected_outcome="不執行自動動作；需要先確認資料來源或等待下一輪具體實證。",
             confidence=0.6,
-            execution_plan=[{
-                "step": 1,
-                "agent": "openclaw",
-                "action": "generate_market_analysis",
-                "parameters": context,
-                "expected_duration": "2-3 minutes"
-            }],
-            resource_requirements={"compute_cost": "$0.00", "time_estimate": "5 minutes"}
+            execution_plan=[],
+            resource_requirements={
+                "compute_cost": "$0.00",
+                "time_estimate": "等待實證",
+                "human_oversight": "required",
+            },
         )
 
 # Singleton instance

services/marketplace_product_matcher.py

@@ -304,6 +304,8 @@ SEARCH_NOISE_TOKENS = {
 }
 
 SEARCH_IDENTITY_ANCHORS = (
+    "潤浸保濕清爽身體乳液",
+    "閃亮珍珠眼影棒",
     "智能光感應無線自動除臭芳香噴霧機",
     "usb精油薰香機",
     "超音波水氧機",
@@ -520,6 +522,8 @@ BRAND_ALIAS_OVERRIDES = {
     "xiaomi": ("小米有品", "小米", "xiaomi"),
     "mac": ("m.a.c", "mac", "m a c"),
     "opi": ("o.p.i", "opi", "o p i"),
+    "curel": ("curel", "珂潤"),
+    "karadium": ("karadium",),
     "st雞仔牌": ("日本雞仔牌st", "日本st雞仔牌", "st雞仔牌", "雞仔牌st", "雞仔牌"),
 }